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GuoleiSun / Indiscernible-Object-Counting

CVPR 2023

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Indiscernible Object Counting in Underwater Scenes (CVPR2023)

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Authors: Guolei Sun, Zhaochong An, Yun Liu, Ce Liu, Christos Sakaridis, Deng-Ping Fan, Luc Van Gool.

1. Object Counting Tasks

The existing object counting tasks include: Generic Object Counting (GOC), and Dense Object Counting (DOC). In this paper, we propose a new challenge termed "Indiscernible Object Counting (IOC)", which focuses on counting foreground objects in indiscernible scenes. The comparisons between different tasks are shown in the following figure.


Figure 1: Illustration of different counting tasks. Top left: Generic Object Counting (GOC), which counts objects of various classes in natural scenes. Top right: Dense Object Counting (DOC), which counts objects of a foreground class in scenes packed with instances. Down: Indiscernible Object Counting (IOC), which counts objects of a foreground class in indiscernible scenes. Can you find all fishes in the given examples? For GOC, DOC, and IOC, the images shown are from PASCAL VOC, ShanghaiTech, and the new IOCfish5K dataset, respectively.

Due to a lack of appropriate IOC datasets, we present a large-scale dataset IOCfish5K which contains a total of 5,637 high-resolution images and 659,024 annotated center points. Underwater scenes contain many indiscernible objects (Sea Horse, Reef Stonefish, Lionfish, and Leafy Sea Dragon) because of limited visibility and active mimicry. Hence, we focus on underwater scenes for our dataset.

2. The Proposed Dataset

The comparisons between our dataset and existing datasets are shown below.

Dataset Year Indiscernible Scene #Ann. IMG Avg. Resolution Free View Total Count Min Count Ave Count Max Count Web
UCSD 2008 ✗ 2,000 158x238 ✓ 49,885 11 25 46 Link
Mall 2012 ✗ 2,000 480x640 ✗ 62,325 13 31 53 Link
UCF_CC_50 2013 ✗ 50 2101x2888 ✓ 63,974 94 1,279 4,543 Link
WorldExpo'10 2016 ✗ 3,980 576x720 ✗ 199,923 1 50 253 Link
ShanghaiTech B 2016 ✗ 716 768x1024 ✗ 88,488 9 123 578 Link
ShanghaiTech A 2016 ✗ 482 589x868 ✓ 241,677 33 501 3,139 Link
UCF-QNRF 2018 ✗ 1,535 2013x2902 ✓ 1,251,642 49 815 12,865 Link
Crowd_surv 2019 ✗ 13,945 840x1342 ✗ 386,513 2 35 1420 Link
GCC (synthetic) 2019 ✗ 15,212 1080x1920 ✗ 7,625,843 0 501 3,995 Link
JHU-CROWD++ 2019 ✗ 4,372 910x1430 ✓ 1,515,005 0 346 25,791 Link
NWPU-Crowd 2020 ✗ 5,109 2191x3209 ✓ 2,133,375 0 418 20,033 Link
NC4K 2021 ✓ 4,121 530x709 ✓ 4,584 1 1 8 Link
CAMO++ 2021 ✓ 5,500 N/A ✓ 32,756 N/A 6 N/A Link
COD 2022 ✓ 5,066 737x964 ✓ 5,899 1 1 8 Link
IOCfish5K (Ours) 2023 ✓ 5,637 1080x1920 ✓ 659,024 0 117 2,371 Link

Table 1: Statistics of existing datasets for dense object counting (DOC) and indiscernible object counting (IOC).

Our dataset can be downloaded from here. It is organized as follows:

    IOCfish5K
    ├── images
        ├──****.jpg
        ├──****.jpg
    ├── annotations
        ├──****.xml
        ├──****.xml
    ├── train_id.txt
    ├── val_id.txt
    ├── test_id.txt

The image ids for train/val/test are in train_id.txt, val_id.txt, and test_id.txt, respectively.

The annotations are in xml format. Each object instance is annotated by a point (x,y coordinates). The point annotation in xml is as follows:

    <object>
        <point>
            <x>x_coor</x>
            <y>y_coor</y>
        </point>
    </object>

3. Benchmarking

3.1. Overview

For benchmarking purposes, we select 14 mainstream methods for object counting and carefully evaluate them on IOCfish5K.

3.2. Usage

To do

4. The Proposed Method

4.1. Overview

we propose IOCFormer, a new strong baseline that combines density and regression branches in a unified framework and can effectively tackle object counting under concealed scenes.

4.2. Usage

For training/inference, please go to here.

5. Results

5.1. Quantitative Results

The results for various methods are shown below.

Method Publication Val: MAE Val: MSE Val: NAE Test: MAE Test:MSE Test:NAE
MCNN CVPR'16 81.62 152.09 3.53 72.93 129.43 4.90
CSRNet CVPR'18 43.05 78.46 1.91 38.12 69.75 2.48
LCFCN ECCV'18 31.99 81.12 0.77 28.05 68.24 1.12
CAN CVPR'19 47.77 83.67 2.10 42.02 74.46 2.58
DSSI-Net ICCV'19 33.77 80.08 1.25 31.04 69.11 1.68
BL ICCV'19 19.67 44.21 0.39 20.03 46.08 0.55
NoisyCC NeurIPS'20 19.48 41.76 0.39 19.73 46.85 0.46
DM-Count NeurIPS'20 19.65 42.56 0.42 19.52 45.52 0.55
GL CVPR'21 18.13 44.57 0.33 18.80 46.19 0.47
P2PNet ICCV'21 21.38 45.12 0.39 20.74 47.90 0.48
KDMG TPAMI'22 22.79 47.32 0.90 22.79 49.94 1.17
MPS ICASSP'22 34.68 59.46 2.06 33.55 55.02 2.61
MAN CVPR'22 24.36 40.65 2.39 25.82 45.82 3.16
CLTR ECCV'22 17.47 37.06 0.29 18.07 41.90 0.43
IOCFormer (Ours) CVPR'23 15.91 34.08 0.26 17.12 41.25 0.38

5.2. Qualitative Results

Qualitative comparisons of various algorithms (NoisyCC, MAN, CLTR, and ours). The GT or estimated counts for each case are shown in the lower left corner.


6. Citations

@inproceedings{sun2023ioc,
    title={Indiscernible Object Counting in Underwater Scenes},
    author={Sun, Guolei and An, Zhaochong and Liu, Yun and Liu, Ce and Sakaridis, Christos and Fan, Deng-Ping and Van Gool, Luc},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision and Patern Recognition (CVPR)},
    year={2023}
}

7. Contact

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CVPR 2023